{"id":"W2991006719","doi":"10.1002/ceat.201900096","title":"Evolving an Accurate Decision Tree‐Based Model for Predicting Carbon Dioxide Solubility in Polymers","year":2019,"lang":"en","type":"article","venue":"Chemical Engineering & Technology","topic":"Polymer Foaming and Composites","field":"Materials Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Solubility; Polymer; Decision tree; Compatibility (geochemistry); Boosting (machine learning); Carbon dioxide; Gradient boosting; Computer science; Process engineering; Chemistry; Materials science; Thermodynamics; Biological system; Machine learning; Organic chemistry; Random forest; Engineering; Physics; Composite material","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002137939,0.0001825464,0.0002821019,0.0002158077,0.00002448895,0.00002008243,0.0004246385,0.000266286,0.000008371855],"category_scores_gemma":[0.0003145444,0.0001876289,0.00004988645,0.0002569168,0.00004786504,0.0001147848,0.0001091483,0.0002163063,0.00000329121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001279628,"about_ca_system_score_gemma":0.00004338223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003009107,"about_ca_topic_score_gemma":0.000004985575,"domain_scores_codex":[0.9986195,0.000006718147,0.0002970581,0.0004737984,0.000121287,0.000481657],"domain_scores_gemma":[0.9992044,0.0001875678,0.00005656332,0.000452349,0.00002931764,0.0000698346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003297603,0.00003803054,0.001455941,0.00003377001,0.000002203464,9.998607e-7,0.00003737317,0.04753939,0.9493304,0.00005954698,6.558537e-7,0.001468675],"study_design_scores_gemma":[0.0002608087,0.00002589513,0.00006086763,0.00005169929,0.000003753372,0.000001360118,0.000008266014,0.5083943,0.4909411,0.0001474074,0.000001354472,0.0001031784],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9604048,0.0002343962,0.03830343,0.0000690607,0.0001661892,0.0001797299,0.000006807983,0.0006029404,0.00003268413],"genre_scores_gemma":[0.9862831,6.567661e-7,0.0135627,0.00001665287,0.00002310429,0.00006690281,0.000005652001,0.00002971562,0.00001155804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4608549,"threshold_uncertainty_score":0.7651286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008380845600628751,"score_gpt":0.2284517533786518,"score_spread":0.2200709077780231,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}